Large-scale discovery, analysis and design of protein energy landscapes - Állan J. R. Ferrari; Sugyan M. Dixit; Jane Thibeault; Mario Garcia; Scott Houliston; Robert W. Ludwig; Pascal Notin; Claire M. Phoumyvong; Cydney M. Martell; Michelle D. Jung; Kotaro Tsuboyama; Lauren Carter; et al. (Nature, 2026)
Abstract All folded proteins continuously fluctuate between their low-energy native structures and higher-energy conformations that can be partially or fully unfolded. These rare states influence protein function 1,2 , interactions 3 , aggregation 4–7 and immunogenicity 8,9 , yet they remain far less understood than protein native states. Although native protein structures are now often predictable with impressive accuracy, conformational fluctuations and their energies remain largely invisible 10 and unpredictable 11–14 , and experimental challenges have prevented large-scale measurements that could improve machine learning and physics-based modelling. Here we introduce a multiplexed experimental approach to analyse the energies of conformational fluctuations for hundreds of protein domains in parallel using intact protein hydrogen–deuterium exchange mass spectrometry. We analysed 5,778 domains 28–64 amino acids in length, revealing hidden variation in conformational fluctuations, even between sequences sharing the same fold and global folding stability. Site-resolved hydrogen exchange nuclear magnetic resonance analysis of 13 domains showed that these fluctuations often involve entire secondary structural elements with lower stability than the overall fold. Computational modelling of our domains identified structural features that correlated with the experimentally observed fluctuations, enabling us to design mutations that stabilized low-stability structural segments. Our dataset enables new machine-learning-based analysis of protein energy landscapes, and our experimental approach promises to profile these landscapes at considerable scale.